scholarly journals Integrating the Supervised Information into Unsupervised Learning

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Ping Ling ◽  
Nan Jiang ◽  
Xiangsheng Rong
Keyword(s):  
Unsupervised Learning ◽  
Nearest Neighbor ◽  
Training Data ◽  
Support Vector ◽  
Second Phase ◽  
Nearest Neighbor Classifier ◽  
Fast Training ◽  
And Performance ◽  
Domain Description ◽  
Neighbor Classifier

This paper presents an assembling unsupervised learning framework that adopts the information coming from the supervised learning process and gives the corresponding implementation algorithm. The algorithm consists of two phases: extracting and clustering data representatives (DRs) firstly to obtain labeled training data and then classifying non-DRs based on labeled DRs. The implementation algorithm is called SDSN since it employs the tuning-scaled Support vector domain description to collect DRs, uses spectrum-based method to cluster DRs, and adopts the nearest neighbor classifier to label non-DRs. The validation of the clustering procedure of the first-phase is analyzed theoretically. A new metric is defined data dependently in the second phase to allow the nearest neighbor classifier to work with the informed information. A fast training approach for DRs’ extraction is provided to bring more efficiency. Experimental results on synthetic and real datasets verify that the proposed idea is of correctness and performance and SDSN exhibits higher popularity in practice over the traditional pure clustering procedure.

scholarly journals Odor identification in novel olfactory environments is selectively impaired in a mouse model of autism

2020 ◽  
Author(s):  
Yan LI ◽  
Mitchell Swerdloff ◽  
Tianyu She ◽  
Asiyah Rahman ◽  
Naveen Sharma ◽  
...  
Keyword(s):  
Mouse Model ◽  
Nearest Neighbor ◽  
Training Data ◽  
Imaging Data ◽  
Wild Type ◽  
Nearest Neighbor Classifier ◽  
Linear Classifier ◽  
Behavioral Paradigm ◽  
Neighbor Classifier

Abstract Aversion to novel stimuli in autism affects quality of life. We developed a behavioral paradigm to study the effect of novel background odors on odor discrimination in mouse models of autism. We trained wild type mice to discriminate target odors in known background odors. When tested, mice could discriminate known targets in novel background odors, a task similar to the visual CAPTCHA used to distinguish humans from computers. Using glomerular imaging data, we showed that WT mice used an algorithm that required less training data than a linear classifier or nearest neighbor classifier. The Cntnap2−/− mouse model of autism matched wild type mice performance in the presence of known backgrounds, but performance fell almost to chance levels in the presence of novel backgrounds. Wild-type mice use a robust algorithm for detecting odors in novel environments and this computation is selectively affected in a mouse model of autism.

scholarly journals Yoruba Handwritten Character Recognition using Freeman Chain Code and K-Nearest Neighbor Classifier

2018 ◽  
Vol 6 (4) ◽  
pp. 129-134 ◽  
Author(s):  
Jumoke Falilat Ajao ◽  
David Olufemi Olawuyi ◽  
Odetunji Ode Odejobi
Keyword(s):  
Character Recognition ◽  
Nearest Neighbor ◽  
Recognition System ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Chain Code ◽  
Nearest Neighbor Classifier ◽  
Digitized Images ◽  
Neighbor Classifier

This work presents a recognition system for Offline Yoruba characters recognition using Freeman chain code and K-Nearest Neighbor (KNN). Most of the Latin word recognition and character recognition have used k-nearest neighbor classifier and other classification algorithms. Research tends to explore the same recognition capability on Yoruba characters recognition. Data were collected from adult indigenous writers and the scanned images were subjected to some level of preprocessing to enhance the quality of the digitized images. Freeman chain code was used to extract the features of THE digitized images and KNN was used to classify the characters based on feature space. The performance of the KNN was compared with other classification algorithms that used Support Vector Machine (SVM) and Bayes classifier for recognition of Yoruba characters. It was observed that the recognition accuracy of the KNN classification algorithm and the Freeman chain code is 87.7%, which outperformed other classifiers used on Yoruba characters.

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